{Reference Type}: Journal Article
{Title}: Short-Term Demand Forecasting of Urban Online Car-Hailing Based on the K-Nearest Neighbor Model.
{Author}: Xiao Y;Kong W;Liang Z;
{Journal}: Sensors (Basel)
{Volume}: 22
{Issue}: 23
{Year}: Dec 2022 3
{Factor}: 3.847
{DOI}: 10.3390/s22239456
{Abstract}: Accurately forecasting the demand of urban online car-hailing is of great significance to improving operation efficiency, reducing traffic congestion and energy consumption. This paper takes 265-day order data from the Hefei urban online car-hailing platform from 2019 to 2021 as an example, and divides each day into 48 time units (30 min per unit) to form a data set. Taking the minimum average absolute error as the optimization objective, the historical data sets are classified, and the values of the state vector T and the parameter K of the K-nearest neighbor model are optimized, which solves the problem of prediction error caused by fixed values of T or K in traditional model. The conclusion shows that the forecasting accuracy of the K-nearest neighbor model can reach 93.62%, which is much higher than the exponential smoothing model (81.65%), KNN1 model (84.02%) and is similar to LSTM model (91.04%), meaning that it can adapt to the urban online car-hailing system and be valuable in terms of its potential application.